Something odd often happens after clinicians order viral load tests for their
HIV-infected patients at William Beaumont Hospital, Royal Oak, Mich.

Or rather, what’s odd is what doesn’t happen: Few order a genotype test
to assess drug resistance.

Carol Holland, PhD, technical director of molecular pathology at the
hospital, says her laboratory performs approximately 30 HIV genotypes
a year. When she worked at nearby Henry Ford Hospital, doing 15 to 20
such tests weekly was the norm. Granted, Beaumont doesn’t have the same
patient population as Henry Ford, which is in Detroit. Nevertheless, Dr.
Holland’s laboratory is fairly big and runs, by her estimate, a couple
hundred viral loads a month. The number of genotypes should not be far
behind.

Genotyping has become standard of care for many HIV-infected patients.
The push isn’t coming from a new set of guidelines. The earliest recommendations
suggested resistance testing could be used in virtually any situation,
and that still holds true. Not all categories merited equal billing, however.
Early on, resistance testing was strongly recommended for patients whose
antiretroviral therapies were failing; less importance was attached to
resistance testing for treatment-naïve patients. What’s emerging now,
say observers, is parity among the classes.

Physicians have begun to make genotyping routine for patients even before
they begin therapy, for example, as evidence grows that drug-resistant
viruses are being transmitted sexually or through IV needle abuse. Reports
suggest that anywhere from 10 to 20 percent of newly infected patients
are infected with drug-resistant virus, while earlier estimates put the
number closer to 10 percent or less.

That may come as news to some. For others, the news isn’t that such transmissions
are occurring, but rather that these resistant viruses are more fit than
previously thought. They’re not being overgrown by wild-type viruses,
as some had predicted. "Now we’ve changed that thinking," says Mark Wainberg,
PhD, professor of medicine, McGill University, and director, McGill AIDS
Center, Montreal. "Because in fact many of the mutations hang around a
lot longer than had previously been assumed."

Marie Hoover, PhD, sees other reasons for clinicians’ interest in genotyping,
including what she calls the let’s-not-use-up-all-of-our-bullets-at-one-time
stance. "They’re realizing the drugs are limited, and that by switching
drugs every time they see the virus start going up, they’ve run out of
drugs for some patients. So they want to see the genotype before they
make therapy changes," says Dr. Hoover, who is CEO and lab director, Advanced
BioMedical Laboratories, Cinnaminson, NJ. On the flip side, she encounters
trigger-happy clinicians who want to change the therapy the minute they
see an increase in viral load. Genotypes can stop an unnecessary switch
in these cases: If there’s no evidence of resistance, the therapeutic
failure is probably tied to noncompliance.

Dr. Holland notes that her pathology residents know when a genotype is
in order—when she trains them, she makes sure they understand when
it should be used, when it’s not appropriate, and how to interpret the
results. "But except for our ID physicians, most of our medical staff
really doesn’t know," she says.

The obvious question to ask is, Why this gap? But given the mazy ways
of HIV resistance, what’s remarkable is that there aren’t more.

It’s not unusual for a new test or technology to have a steep learning
curve. As the method itself evolves, it usually becomes easier. It’s also
not unusual for users to catch on as they gain experience with the new
method.

Not so with HIV genotyping. Knowledge has remained in the hands of relatively
few. "Clinicians who live and breathe this are more comfortable doing
the interpretations themselves," says David Hillyard, MD, associate professor
of pathology, Department of Pathology, University of Utah School of Medicine,
and director of molecular infectious disease testing at ARUP Laboratories,
Salt Lake City. Those who treat HIV patients have to be familiar with
an extremely complicated pharmacology, thanks to the wide number of drugs
and various interactions. For this group, "I’m sure there is more clarity
about resistance testing than there was even several years ago," says
Dr. Hillyard. He also concedes that reporting of resistance has perhaps
become slightly more clear in recent years. Nonetheless, the vast majority
of clinicians do not have sophisticated knowledge of mutations, how they
work, nor what their impact is.

For them, HIV genotyping may always be somewhat of a Sisyphean adventure.

HIV is a swarm of different, constantly evolving viruses, and resistance
changes all the time. A genotype is a mere snapshot into one little window
of time, showing the dominant virus seen and sequenced at that moment.
There will always be unknown mutations that can’t be accounted for, and
there will always be unpredictable interactions among mutations.

Physicians work from a current list of mutations that have been identified
as being predictive of resistance. But who knows how long the "real" list
is? "It’s still kind of primitive," says Larry Reimer, MD. "We’re looking
at a short list right now, when in reality there conceivably is a long
list." Or maybe there’s no list at all. Perhaps the collective structure
of the virus total is what matters, and not these individual site mutations,
suggests Dr. Reimer, director of the microbiology laboratory and head
of the section of infectious disease at the VA Medical Center, Salt Lake
City; medical consultant to the HIV resistance testing laboratory at ARUP;
and associate dean for curriculum and graduate medical education, University
of Utah School of Medicine.

Genotype reports vary somewhat, depending on which system the laboratory
is using. There are two FDA-approved systems, the VGI/Bayer TruGene and
the Abbott (ABI/Celera) ViroSeq, in addition to home brews. At this point,
the various systems have much in common. Says Dr. Hoover: "Really and
truly, everyone’s sequencing everything, the same regions of the HIV."
But not everyone reports the same data. That’s where the algorithms come
in.

The interpretation of a genotype report is driven by algorithms—a
different one for each system. And even though "there’s tremendous similarity
between them," as Dr. Hillyard puts it, they’re not identical. Anytime
a new observation is made in the literature, and anytime a new drug becomes
available, there will be differences in accommodating that information
into the algorithms. "It is a problem," Dr. Hillyard says.

Reports typically include a citation of the resistance mutations that
have been found by sequencing, as well as sensitivity/resistance or likely
sensitivity/likely resistance. For a physician who truly understands the
meaning of a particular mutation and is familiar with the current literature,
that may be enough. "They can look and see there is a mutation that would
change an amino acid at this codon position, for example," explains Dr.
Hillyard. But such physicians are few and far between. At ARUP, he says,
the HIV resistance testing laboratory engages a practicing clinician as
its medical director, "because it is just so incredibly complicated now."

Reports also contain a summary and a prediction of whether a patient
will be resistant or sensitive to specific drugs. This information comes
from an algorithm of analyzing the sequence data, the rules of which are
put together based on recommendations from a panel of experts. Those experts,
says Dr. Hillyard, are treating physicians and others steeped in the latest
research, who understand the implications of nucleic acid changes. Those
changes alone reveal nothing-a genotype, after all, is completely dependent
on correlation with in vitro phenotype studies and with actual clinical
studies.

What reports don’t contain, says John Baxter, MD, is guidance on whether
one drug is necessarily more appropriate than another drug, or what combination
of drugs to use. Eventually that information may become available via
a Web-based resource. The nonprofit HIV Resistance Response Database (hivrdi.org)
is developing a computerized system that will offer suggested treatment
regimens based on the mutations and treatment history provided by clinicians.
"It’s sort of an artificial intelligence system, using neural networks
to ’train’ this computer to be able to produce these best or likely most
effective regimens," says Dr. Baxter, associate professor of medicine,
Division of Infectious Diseases, Cooper Hospitals, University of Medicine
& Dentistry of New Jersey.

The experts who update the algorithms may have significant differences
of opinion regarding new data—how it should be interpreted and what
weight to give it. "For something as complicated as HIV genotype testing,
it’s probably impossible to have just a single algorithm that’s standardized
for everybody," Dr. Hillyard says. Despite the fact that everyone is basically
on the same, complicated page, "there are cut-out areas where there is
vigorous debate what the significance of a given mutation is."

What these experts talk about—and for that matter, what any HIV
specialist needs to be aware of—is that nothing stands still. HIV
is like an endless series of switchbacks. They take you up, they take
you back down, and sometimes they just make you sit down in the middle
of the trail and cry.

New mutations can and do appear. So do new drugs. Then come new rounds
of interactions and what they mean. "That’s why I won’t interpret genotypes
myself," says Dr. Holland. "It’s more accurate and time-effective to use
algorithms derived by a panel of experts."

The next thing to come down the pipe will likely be drugs that target
something other than protease and reverse transcriptase—the very
things sequenced by genotypes. These days there are also frequent conversations
about so-called pattern recognition and which pattern best predicts lack
of activity. Particularly within the nucleoside reverse transcriptase
inhibitors, there’s a "split" between TAMs (thymidine analog mutations)
versus non-TAMs. This is potentially valuable information that has yet
to find a practical place in genotype reports. Replicative capacity is
another hot topic—but only a phenotype, not a genotype, can provide
insight into the virus’ fitness.

Dr. Baxter, who sits on the panel of experts (as does Dr. Wainberg) that
updates the guidelines for Bayer’s TruGene system, talks about why their
job is so hard. "You’ve got the pull of wanting the most accurate rules
based on published literature on the one hand. On the other hand, information
is rapidly being produced and presented, and you want to get that information
into the rules as well, because it changes the way we understand resistance.
So it’s a tug-of-war. And different algorithms may be accurate but out-of-date."

Sometimes the debate is whether to include new but unpublished information.
"There may be a couple of abstracts, which suggests an important new finding.
So that’s really a judgment call. We take it case by case," Dr. Baxter
says.

Stirring things up a bit more, Dr. Reimer asks, "How good is the information
we have to say that doing resistance testing makes any difference in how
all patients will do?" Taking a poke at current thinking, he notes that
a recent meta-analysis of genotype and phenotype studies hinted that in
long-term patient outcome, the answer was, "Not much."

That’s not necessarily discouraging news, he says, because the studies
under review were older, and interpretative success may be higher today.
But he suggests it may be premature to make rock-hard pronouncements about
the value of genotyping and phenotyping. "Most of the outcomes data that’s
been presented is simply on viral load numbers; it’s not about disease
progression, death, opportunistic infections, things like that," he says.

That brings him to his closing argument. "There’s a huge bandwagon for
resistance testing: ’Oh, this is critically important, it’s standard of
care, it ought to be done.’" And maybe it should, he agrees. "But I would
say we should take a healthy, skeptical view of all of this. Because we
really don’t know that it improves patient care. Physicians with a lot
of experience, who are expert in HIV management, may do close to as well
as testing does."

The problem is so few physicians fall into that category, says Dr. Hoover,
who finds it frustrating when physicians fail to use genotyping information.
Dr. Hoover was an author on the GART study, one of the first controlled
trials of resistance testing. That trial showed a clear benefit, based
on virologic response, of selecting therapy based on resistance assays.
"I’ve talked to clinicians who just don’t use it," she says. "Some actually
get the information as part of a clinical trial but have told me point-blank,
’We don’t use that for treating the patient.’

Richard Haubrich, MD, associate professor of medicine, University of
California, San Diego, spends about 80 percent of his professional life
designing, conducting, and analyzing clinical HIV trials. Here’s how he
breaks down the evidence for using resistance tests:

First, some 20 to 30 different retrospective analyses have explored whether
viral resistance, defined by either a genotype or phenotype, predicts
reduced response (in terms of HIV RNA reduction) to a new antiretroviral
regimen. "The answer, almost unequivocally, is ’Yes,’" Dr. Haubrich reports.

The second step was to look at whether resistance testing helps clinicians
select a better regimen. Ten to 15 randomized studies looking at this
have, in Dr. Hau brich’s opinion, yielded another explicit "Yes." Resistance
testing, he says, does improve clinicians’ ability to select the next
regimen. The endpoints of those studies are, again, better suppression
of HIV, usually defined by viral load changes or proportion less than
detectable, Dr. Haubrich notes.

There are other debates, of course, but most clinicians don’t care about
the issues that get laboratorians talking—in this case, whether
a system uses a slab gel or capillary electrophoresis method. Nor do they
need to. "I think, as with most forms of testing, clinicians basically
assume that the results are generated by a gradually improving and, hopefully,
state-of-the-art method," Dr. Hillyard says.

For the record, Dr. Hillyard reports that in recent years there has been
a migration toward capillary electrophoresis, accompanied by newer generations
of reagents. Overall, he says, the quality of sequencing has gone up.
And Dr. Reimer adds that genotype tests have become highly accurate in
recent years. He recalls samples being sent to multiple labs four or so
years ago, which revealed "a lot of discrepancy from one lab to another
on whether they could detect mutations. I think that’s pretty much resolved
now with the kits from the two companies."

What remains discrepant, from lab to lab, is the interpretation.

Some laboratories take the position that theirs is an in-house interpretation,
based on in-house information that uses published data. And that’s fine,
Dr. Hoover says. "But the docs need to understand that the same sequence
may not provide the same interpretation, depending on the interpretation
algorithm the testing laboratory is using."

It’s also worthwhile for laboratories—and probably their clinicians—to
know which general method is being used. Anyone who reads the clinical
trials and the primary literature will see that the studies are done using
a particular reagent and accompanying algorithm, says Dr. Hillyard. "And
it’s probably worth understanding what the laboratory’s experience has
been in terms of what the limits of detection can be. Most laboratories
can get down to 500 copies or so—but does that laboratory comfortably
interrogate a low sample that has 500 copies?" Such information should
be published in the laboratory’s user’s guide. Dr. Hillyard notes some
clinicians may not appreciate the potential for sampling problems that
arise near the cutoff levels. "You may get into trouble if you push the
envelope down to the very, very lowest possible viral load levels with
these methods," he says.

Dr. Holland, at William Beaumont Hospital, says she occasionally has
difficulty explaining to referring clinicians that viral loads may simply
be too low to obtain a useful genotype. In one case, she recalls, a clinician
"argued with three techs before she got to me, because her patient had
a viral load of 81, and we wouldn’t do the genotype." The previous viral
load had been 79, and the clinician viewed the uptick as evidence of therapeutic
failure. "I said, ’They’re not failing—they’re doing really, really
well. I’m not going to be able to help you. It’s [the genotype] not going
to be accurate, because we’re not going to be able to pick up minor species
with that low of a viral load.’

"And then she got so mad at me she slammed the phone down," says Dr.
Holland, who’s quick to add this response isn’t typical.

That blind spot is not exclusive to clinicians, she says. Before genotyping
was brought onboard at Beaumont, she and her colleagues sent their samples
to a reference lab, which, she says, would do a genotype on any sample
regardless of how low the viral load was.

Laboratories no doubt appreciate this, but clinicians may not: Interpretation
is not black-and-white. "You have to look at patterns, and to understand
what their implications are," says Dr. Reimer. Clinicians also don’t always
appreciate the basic biology of the virus and the mutability of its configuration,
which can alter the sites of drug attachments, he says.

"Clinicians need to understand that, as a first principle, these tests
absolutely do not stand alone," says Dr. Hillyard. It’s not a shortcut,
allowing physicians to prescribe or change therapy based on a simple reading
of the sensitivities/resistances. Clinicians need to consider what the
current therapy is, what past therapies have been, and what prior resistance
testing has shown, he continues. "There are, for a given patient, ways
of estimating what likely drug resistance you may encounter. And you have
to have thorough knowledge of drug interactions. So once you’re given
this report, you’re just beginning to challenge your own thinking process
as a clinician to use the data."

"The level of complexity of just doing this in the first place, then
throwing in the idea that the different methods may not agree with each
other, makes it very complicated for docs to try to interpret," says Dr.
Reimer. Add to that the fact that there nothing intuitive about genotyping,
though that may change as labs use gene analyses to look for resistance
in other organisms. That’s already starting to happen with staph and tuberculosis,
Dr. Reimer points out. But for now, the antibiotics/susceptibility worldview,
which relies on phenotyping, holds sway.

Most clinicians probably do understand that these assays sequence a population.
The commercial assays will pick up a resistant variant that’s present
in perhaps 30 percent or more of the population of a patient’s virus.
Finding archived specimens requires ultraspecialized techniques.

Another seemingly obvious but perhaps overlooked point is that any type
of resistance testing needs to be done when the patient is adhering to
therapy. Otherwise, the assay may not reflect the actual resistance of
that patient’s virus—the wild type may re-emerge, giving the clinician
false assurance that the patient will respond to specific medications.

Clinicians might not appreciate the laboratory’s own possible limitations
in providing interpretations. Less-experienced labs may be supplying a
software-based interpretation and nothing else, says Dr. Haubrich. "Unless
the laboratory director is really interested in HIV resistance testing,
he’s probably not going to add to that." In these cases, if clinicians
don’t have a good idea of how to interpret the results, they should seek
expert opinion. "Resistance testing is helpful, but it’s also important
to know how to use the information," says Dr. Baxter.

In some centers, clinicians have relatively easy access to such experts,
and can bring the difficult questions or cases to them directly. Occasionally
they may call on the laboratory. There are also Web-based sources, such
as Stanford University’s HIV drug resistance database (hivdb.stanford.edu),
where clinicians can plug in mutations from a genotype report and be given
an interpretation and comments.

There’s also the possibility that the need for expert opinion may eventually
fade. While clinical trials have demonstrated such need, the technology
was relatively new when those trials were done, so the expert advice would
have added value that may not be needed in the future. But who knows for
sure?

Hovering nearby is another related test: virtual phenotyping. Virtual
phenotypes are, essentially, enhanced genotype reports. They aid interpretation
of the genotype by finding close—not exact—matches to the
patient’s genotype in a large database. The average phe no type for matched
genotypes is reported.

"I haven’t seen clear studies that show it’s better than genotyping,"
says Dr. Hoover. "So my question is, why are we converting to something
that doesn’t have a track record? I think a lot of it is because they
[the clinicians] like the way it looks. Phenotype is something the docs
are used to, because they do it for microbes.

"At least the genotyping has been through peer review, because there
are two FDA-approved genotyping systems," she continues. She’s less sure
about virtual phenotyping. "However, I’m not the clinician out there with
the patient, and the virtual phenotype may be helping those clinicians
make decisions. I guess that’s the difference between coming from the
laboratory side, and the science side, versus the clinical side."

Given these chaotic discussions, it’s not surprising there are tremendous
differences from practice to practice in how genotypes are ordered. But
perhaps the greatest impediment has nothing to do with medicine or biology.
"By far, the biggest reason why resistance testing is not done is cost,"
says Dr. Reimer. "Nothing else comes even close."

True, the test is reimbursable. But that doesn’t necessarily help those
in public clinics that rely heavily on state or federal funding. "Those
programs are really hurting these days," observes Dr. Reimer. Genotyping,
an expensive test, becomes an obvious, if not ideal, target.

In California, reports Dr. Haubrich, money woes have forced physicians
to make bleak choices. "We have caps on how many resistance tests we get,
so in the clinic we sit there and say, ’OK, am I going to test all these
naïve patients, or am I going to test someone who’s been on therapy and
likely has resistance? It’s tough."

When cost doesn’t trip up clinicians, lack of self-confidence can, says
Dr. Wainberg. "Some physicians won’t order a test if they’re not sure
how to interpret it. It’s almost as though they’re intimidated, quite
honestly," he says.

The solution, in his view, is to educate clinicians and patients alike
about the importance of genotyping. Patients should insist on a genotype
before therapy is started, in his view. And laboratories, he says, "should
do a selling job to the clinicians they serve, in terms of always keeping
them abreast as to the importance of requesting a genotype." Labs, he
says, are in the ideal position to back up a request that more genotypes
be done, with actual data that document the importance of genotypes and
phenotypes.

The word "snapshot" comes up frequently in discussions about HIV genotypes.
It’s an interesting analogy.

The earliest tools of HIV testing, CD4 counts and viral loads, coupled
with clinical progression and outcome, were insufficient, recalls Dr.
Hillyard. Thus, any new tool—in this case, genotyping—would
have been anticipated as potentially having tremendous value, particularly
since resistant viruses were seen as trouble early on. "Genotyping was
anticipated very eagerly," says Dr. Hillyard.

Given that anticipation, a letdown was almost unavoidable. As genotyping
made clinical inroads, the results have not had the same earth-shaking
impact that viral load has had on monitoring. Studies looking at genotyping’s
impact have been positive, but not overwhelmingly so, says Dr. Hillyard.
Patients whose treatment plans used genotyping results had viral load
measurements half a log lower than those without. That’s an important
change—but hardly the all-or-nothing effect many hoped for.

Because genotyping is so complicated, and so difficult, it may
never be the breakthrough so many have hoped for. It may remain a snapshot,
and, like a snapshot, overlooked by some and valuable to others.